Distributed Intelligence: Extending the Power
of the Unaided, Individual Human Mind
Gerhard Fischer
University of Colorado, Center for LifeLong Learning and Design (L3D)
Department of Computer Science, 430 UCB
Boulder, CO 80309-0430 – USA
http://l3d.cs.colorado.edu/
gerhard@colorado.edu
ABSTRACT
The history of the human race is one of increasing intellectual
capability. Since the time of our early ancestors, our brains have
gotten no bigger; nevertheless, there has been a steady accretion
of new tools for intellectual work (including advanced visual
interfaces) and an increasing distribution of complex activities
among many minds. Despite this transcendence of human
cognition beyond what is “inside” a person’s head, most studies
and frameworks on cognition have disregarded the social,
physical, and artifactual surroundings in which cognition and
human activity take place.
and lifelong learning. Our fundamental assumptions and
objectives relevant to the framework of this paper are:

Focusing on Intelligence Augmentation (IA) rather than on
Artificial Intelligence (AI) by empowering human beings
rather than replacing them [Fischer & Nakakoji, 1992;
Terveen, 1995];

Exploiting the power of the human visual system [Boecker et
al., 1986] [Boecker et al., 1991] by creating task- and userspecific visualization;

Distributed intelligence provides an effective theoretical
framework for understanding what humans can achieve and how
artifacts and tools can be designed and evaluated to empower
human beings and to change tasks.
Providing support not only to individuals but to groups and
communities, and thereby exploiting the power of social
creativity based on informed participation [Fischer et al.,
2005];

This paper presents and discusses the conceptual frameworks and
systems that we have developed over the last decade to create
effective socio-technical environments supporting distributed
intelligence.
Contextualizing generic systems to person- and task-specific
environments to account for a “universe of one” by
supporting meta-design, customization, and end-user
development [Fischer & Giaccardi, 2006];

Transcending “gift-wrapping” and “techno-determinism” as
isolated and one-sided design objectives for new media by
pursuing co-evolution among (i) new media; (ii) new theories
about working, learning, and collaborating; and (iii) the
creation of a new learning organization in a synergistic
approach [Brown & Duguid, 2000; Fischer, 1998];

Exploring “computing off the desktop” in different directions
[Fischer & Konomi, 2005] by
Categories and Subject Descriptors
H.5.3 Group and Organization Interfaces
General Terms
Design, Experimentation, Human Factors
1. INTRODUCTION
Inventing and creating good visualization is an important
objective for numerous intellectual activities: thinking, working,
learning, and collaboration. Advances in human cognition and
intelligence have been made by powerful representations [Larkin
& Simon, 1987] [Donald, 2004] — of which visualizations are a
specific class (including such specific examples as Napoleon’s
March to Moscow [Tufte, 1983], Simon’s analysis of strengths
and weaknesses of different representations [Simon, 1996], and a
collection of papers using vision to think [Card et al., 1999]).
Distributed intelligence is a conceptual framework that includes
visualizations but has additional dimensions. Over the last decade,
the Center for LifeLong Learning and Design [L3D, 2005] has
developed a research agenda focused on distributed intelligence
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going small: socio-technical environments supported by
personalized, portable devices and wireless
communication that afford information and
communication among people as they move around in
the world — the specific application context being the
CLever project [Carmien et al., 2005];
o
going large: large computational tables that allow
people from diverse backgrounds to access, contribute
to, and interact with information in an inherently social
manner to support collaborative work among others in
shared physical locations — the specific application
context being the Envisionment and Discovery
Collaboratory [Arias et al., 2000].
2. DISTRIBUTED INTELLIGENCE
In most traditional approaches, human cognition has been seen as
existing solely “inside” a person’s head, and studies on cognition
have often disregarded the physical and social surroundings in
which cognition takes place. Distributed intelligence [Hollan et
al., 2001; Salomon, 1993] provides a theoretical framework for
understanding what humans can achieve and how artifacts, tools,
and socio-technical environments [Mumford, 1987] can be
designed and evaluated to empower human beings and to change
tasks. Distribution can take place

among people — the rationale for it being: (1) “symmetry of
ignorance” (being knowledgeable in some domains and not
in others); (2) exploring new divisions of labor and
developing new specializations; and (3) engaging
stakeholders in collaborative learning and working efforts;

between humans minds and artifacts — the rationale for it
being: (1) complementing humans with computational
capabilities; (2) developing “things that make us smart”; (3)
creating powerful external representations (including
advanced visualizations); and

as the integration of these two dimensions of distribution —
the rationale for it being: (1) creating tools for collaboration;
(2) enhancing social creativity; (3) supporting reflective
practitioners.
The critical importance of externalizations (and oeuvres [Bruner,
1996]) are: (1) they produce a record of our efforts, one that is
“outside us” rather than simply in memory; (2) they make our
thoughts and intentions more accessible to reflective efforts
[Schön, 1983]; (3) they produce situations that talk back to us
(e.g., in the form of critiquing systems [Fischer et al., 1998]).
Externalizations that represent works-in-progress are of special
interest, especially when all stakeholders are empowered to make
active contributions to the evolution of artifacts [Fischer &
Giaccardi, 2006], thereby producing and sustaining creativity with
shared and negotiable ways of thinking in a group.
There are two major perspectives [Norman, 1993] conceptualizing
distributed intelligence:

The personal point of view: In this perspective, distributed
intelligence changes the nature of the tasks that human
beings have to do by creating new divisions of labor (e.g., the
work that check-out clerks in a supermarket or airplane pilots
have to do today compared to 30 years ago);

The system point of view: In this perspective, the combined
“human(s) and artifacts” system [Engelbart, 1995] is more
powerful, is more reliable, and can achieve tasks that none of
the components could achieve by themselves. As Einstein
remarked: “My pencil is cleverer than I” (e.g., socio-
technical environments for people with cognitive disabilities,
modern cockpit (including pilot and computers) of an
airplane).
Numerous claims and arguments can be found to indicate the
necessity for a distributed intelligence perspective, including:

human mental activity is neither solo nor conducted
unassisted, even when it goes “inside the head” [Bruner,
1996];

“how the mind works” is itself dependent on the tools at its
disposal (analogous to “how the hand works” not being fully
appreciated unless one takes into account whether it is
equipped with a screwdriver, a hammer, or a pair of scissors)
[Bruner, 1996];

brain-culture symbiosis, which states that the human brain
cannot realize its potential unless it is immersed in a
distribution network [Donald, 2004]. This brain-culture
symbiosis can lead to “higher intelligence” with the support
of “mind tools” that allow people to think previously
unthinkable thoughts by integrating the raw intellectual
power of the human brain with appropriate technologies; and

material culture, which externalizes memory and greatly
amplifies the permanence and power of distributed cognition,
and frees the symbolization process from the limitations of
biological memory [Donald, 2004].
2.1 Distributed Intelligence in a Historical
Context
Distributed intelligence is a fundamental framework by which to
marry the raw intellectual power of the human mind with
appropriate technologies [Donald, 2004]. Skills related to literacy
[Ong, 1982] are the most important contribution. There is no
doubt that information and communication technologies have
created fundamental new design possibilities, but their true
strengths and weaknesses need to be further explored. Figure 1
provides a graphical illustration of some of the major steppingstones toward increasing the power of the collective human mind
aided by technology (as an extension to Figure 1, the brief
analysis of “Faustian Bargains” in section 5 is a reminder that new
technologies will not necessarily lead to some monotonically
increasing power of the collective human mind).
Figure 1: Beyond the Unaided Individual Human Mind
2.2 Tools for Learning and Tools for Living
An important specific classification within a distributed
intelligence framework is to differentiate between tools for living
(which remain part of our environment and our activities) and
tools for learning (which often serve as a scaffolding mechanism
and are designed and employed to fade away over time) [Carmien
& Fischer, 2005; Pea, 2004].
Tools for learning support people in learning a new activity with
the objective that they will eventually become independent of the
tool. Tools for learning afford an internalization of what was (if it
existed previously at all) an ability supported by external
mechanisms. Examples of tools for learning are bicycles with
training wheels or toddlers’ walkers.
Tools for living are artifacts that empower human beings to do
things that they could not do by themselves. They support
distributed intelligence. Examples of tools for living include
eyeglasses, the telephone, screen readers for blind people,
visualization tools, and adult tricycles. No matter how many times
people use the phone to talk to friends across town, their native
ability to converse over long distances unaided remains the same.
Tools for living allow people with disabilities to perform tasks
that they would not able to accomplish unaided, and therefore
allow these people to live more independently.
Whether a tool is a tool for learning or a tool for living is in many
cases not an attribute of the tool itself, but is determined by the
use context and the objectives of the user. Wizards used in many
computational environments, spelling correctors, and handheld
calculators can serve both purposes with different trade-offs.
Learning to live and act without a tool will create an
independence of the tool and may lead to a deeper understanding
of the activity itself, but this will often come at considerable costs,
including time and effort in learning the activity and then
ultimately executing it in a possibly more error-prone and timeconsuming way compared to using the tool.
2.3 “Eyeglasses” for the Mind
Distributed intelligence approaches support the vision and object
that anatomy and cognitive abilities are not destiny. An important
intellectual or philosophical grounding of this mission is provided
by Postman [Postman, 1985]: “The invention of eyeglasses in the
twelfth century not only made it possible to improve defective
vision but suggested the idea that human beings need not accept
as final either the endowments of nature nor the ravages of time.
Eyeglasses refuted the belief that anatomy is destiny by putting
forward the idea that our minds as well as our bodies are
improvable!” The observation that “our minds are improvable”
through media and technologies by constructing “eyeglasses for
the mind” has led to the following objectives for our research with
people with cognitive disabilities:

the assertion that the cognitive abilities of all of us are
limited (the most convincing example being the limitations
of our memories addressed by the invention of reading and
writing);

the development of computational media that provide unique
opportunities to “improve our minds” (and especially the
minds of people with cognitive disabilities), leading to
fundamental research challenges in media as extensions of
humans [Engelbart, 1995; McLuhan, 1964; Norman, 1993].
3. SOCIO-TECHNICAL ENVIRONMENTS
BASED ON A DISTRIBUTED
INTELLIGENCE FRAMEWORK
This section describes two major development efforts at the
Center for LifeLong Learning and Design. These evolving sociotechnical environments are grounded in a distributed intelligence
framework and at the same time contribute to the further
development of that framework.
3.1 Cognitive Levers: Improving Minds with
Distributed Intelligence
The CLever (“Cognitive Levers: Helping People Help
Themselves” [CLever, 2005]) research project develops sociotechnical environments [Mumford, 1987] to support persons with
cognitive disabilities and their caregivers. These environments are
designed to allow people with disabilities to perform tasks that
they would not be able to accomplish unaided. The objective is to
make people more independent by assisting them to live by
themselves, use transportation systems, interact with others, and
perform a variety of domestic tasks. CLever’s goal is to create
more powerful media, technologies, and communities to support
new levels of distributed intelligence. Research in CLever
includes the following specific environments: (1) human-centered
public transportation systems, and (2) end-user development
environments for prompting systems needed in this environment.
The technologies developed in the CLever project will be broadly
available as dual-use technologies applicable to a variety of
different application areas.
Mobility-for-All: Human-Centered Public Transportation
Systems. Public transportation systems are among the most
ubiquitous and complex large-scale systems found in modern
society. For those unable to drive, such as persons with cognitive
disabilities, these systems are gateways for participation in
community activities; they increase their opportunities for
socialization, and they can provide a level of independence from
other human beings. The “Mobility-for-All” project [Carmien et
al., 2005] embedded in CLever is creating mobile architectures
and prototypes to support persons with cognitive disabilities and
their caregivers. This research has broad implications for
designing more human-centered transportation systems that are
universally accessible for other disenfranchised communities,
such as the elderly [National-Research-Council, 2004], nonnative
speakers, and infrequent users of public transportation systems.
Our field studies (analyzing current public transportation systems
in several major cities) have suggested two major design
strategies for creating a Mobility-for-All public transportation
architecture: (1) design components that simplify the complex
navigational artifacts encountered in public transportation
systems; and (2) design architectures and components that
transcend the need to understand complex artifacts and serve as a
dynamic, contextualized navigational assistant. Our research is
focused on the second design approach, and it explores
specifically:

technologies for mobile users: directly supporting the mobile
user (specifically the person with cognitive disabilities)
equipped with a PDA or mobile phone with personally
relevant navigational tasks, including selecting a destination,
locating the right bus, preparing to board, boarding the bus,
signaling the driver where to get off, and disembarking;

technologies for caregivers: when needed, initiating or
facilitating communications between the mobile user,
support communities, and transportation system operators;
providing a “safety net” when something goes wrong.
The Mobility-for-All prototype provides personalized and
contextualized assistance to address the unique “universe of one”
[Fischer, 2001] problems of travelers with cognitive disabilities
(people with disabilities more than those without form a “universe
of one” in the sense that their abilities are unique). Human beings
have internal scripts in their heads (representing tools for
learning) that can be complemented by external scripts
(representing tools for living).
Memory Aiding Prompting System (MAPS). MAPS [Carmien,
2006] is an end-user development environment supporting the
creation of external scripts tailored to distribute cognitive tasks
(such as traveling, cooking, etc.) by complementing a user’s
abilities. Scripts consist of memory prompts with task-specific
visual and auditory stimuli and feedback. They are organized as
finite state sequences with state changes triggered by the user’s
actions (selection from a menu, movement, etc.) or external events
in the environment (arrival of a bus, passage of time, etc.).
Figure 2: Mobility-for-All — An Agent-based Prototype
Figure 2 shows an agent-based prototype environment integrating
a mobile prompting device (to be used by the mobile user)
synchronized with a virtual 3D display of a real-time bus system
(to be used by the caregiver) [Repenning & Ioannidou, 2006].
One key design parameter in developing scripts is the granularity
and specificity of a particular prompt and feedback sequence.
When learning to travel independently, the prompting granularity
may be very detailed, with frequent prompts and feedback. As the
user gains experience, granularity of instructions can become
more coarse-grained to reflect learning and be less intrusive (to fit
the requirement of a tool for learning).
Figure 3: MAPS —An End-User Development Environment for External Scripts
The scripts needed to effectively support users are specific for
particular tasks, creating the requirement that the people who
know about these tasks (the local caregivers and not some
technologists far removed from the action) must be able to
develop scripts. Caregivers generally have no specific
professional technology training nor are they interested in
becoming computer programmers. This creates the need for
design environments with extensive end-user support to allow
caregivers to create, store, and share scripts [Fischer & Giaccardi,
2006]. Figure 3 shows the prototype of a caregiver configuration
environment (embedded in MAPS) for creating complex,
location-aware, multimodal prompting sequences. The prototype
allows sound, pictures, video, and so forth to be assembled by
using a film-strip-based scripting metaphor.
The design of the script editor presents interesting challenges for
meta-design, a process for creating new media and environments
that allow users to act as designers [Fischer, 2002; Fischer et al.,
2004]. The caregiver script editor will support the specification of
contextual tests that trigger specific interventions and guide the
MAPS user back on track, or notify the caregiver when
appropriate. Enabling non-programmers to create complex scripts
with embedded error trapping and correction information
constitutes a complex meta-design challenge, and a major
emphasis in the overall script editor design.
3.2 Envisionment and Discovery
Collaboratory
The Envisionment and Discovery Collaboratory (EDC) [Arias et
al., 2000] is an environment in which participants collaboratively
solve problems of mutual interest. The problem contexts explored
in the EDC, such as urban transportation planning, flood
mitigation, and building design, are all examples of open-ended
social problems. The EDC empowers users to act as designers in
situated learning and collaborative problem-solving activities. For
most design problems, the knowledge to understand, frame, and
solve them does not already exist, but is constructed and evolved
during the solution process, exploiting the power of the
“symmetry of ignorance” and “breakdowns.” The EDC is an
environment in which social creativity can come alive [Fischer,
2000].
by using technologies that recognize the placement and
manipulation of physical objects. Computer-generated
information is projected back onto the horizontal physical
construction area, creating an augmented reality environment.
This physical construction is coupled with information relevant to
the problem currently being discussed.
The collaborative design activities supported by the EDC rely
upon the contribution and active participation of all involved
stakeholders [Fischer, 2002]. Design domains consist of illdefined and wicked problems [Schön, 1983], for which there are
no correct answers, and in which framing problems is a major
aspect of problem solving. The collaboration of stakeholders is an
inherent aspect of these domains. For example, citizens, urban
planners, and transportation experts who solve or are influenced
by urban transportation issues are themselves an integral part of
the problem context. The goal of the EDC is to bring together
these stakeholders (each one or each group owning one part of the
problem) to solve problems of mutual interest and to take active
roles in addressing the problems that shape their lives.
Figure 5 shows the integration of the EDC with Google Earth. The
participants have created sketches of new buildings with a specific
height in their design, and Google Earth is used to show the
impact of these actions (e.g., how much they block the view of the
mountains — a major controversial issue in the City of Boulder,
Colorado).
Figure 5: Integrating the EDC with Google Earth
The EDC has allowed the exploration of different dimensions of
distributed intelligence, including [Fischer et al., 2005]:

Figure 4: Stakeholders Collaborating by Using the EDC
Figure 4 shows the EDC in use. The stakeholders (representatives
from the Boulder City Council and the Regents from the
University of Colorado) discuss and explore problems within a
shared construction space in which they interact with physical
objects that are used to represent the situation currently being
discussed. As they manipulate physical objects, a corresponding
computational representation is created and incrementally updated
Articulating tacit knowledge. Often, critical aspects of the
perspectives being brought to the EDC are based on
knowledge not previously externalized and formalized.
These are sometimes individual perspectives that have been
experienced but never expressed. By allowing shared
interaction through a descriptive process, the EDC supports
methods whereby such perspectives can be drawn out and
brought to the table, enhancing individual and collective
contributions to the creative process. The overall design of
the EDC, targeted toward these participants, employs the use
of physical objects to create an inviting and natural
interaction with the simulation, and recognizes that parallel
interaction capability is essential to support this natural
interaction [Eden, 2002]. The development of active critics
[Fischer et al., 1998] and virtual stakeholders [Arias et al.,
1997] supports informed participation.


Creating bridge objects. One danger of any model or
conceptualization is that it may embody certain assumptions
and perspectives that, if not questioned, can lead to an
imbalance of influence within the process. These forms of
model monopoly [Bråten, 1983] and “group-think” [Janis,
1972] need to be balanced by having open representations of
the models and by supporting epistemological pluralism
[Turkle & Papert, 1991] that allows for deeper
understanding, experimentation, and possibly refutation. The
goal is to permit a migration toward shared representations
that are useful across contexts and communities as bridge
objects [Bowker & Star, 2000]. The EDC design goals are to
provide an open environment and design process that will
allow these models to be developed and extended.
Supporting meta-design. Creating models within the EDC
requires a considerable amount of programming effort. This
represents a high degree of reliance upon high-tech scribes,
distancing the real designers from the medium of expression.
Environments (even domain-oriented ones) that are open and
easily modifiable and extensible are still elusive. While we
continue to work on support for end-user development and
meta-design [Fischer & Giaccardi, 2006], we are also
looking at ways to harness existing tool use, integrate with
existing practice, develop models (inspired by open source
software development methodologies [Raymond & Young,
2001]), and empower local developers [Nardi, 1993].
Working from a meta-design perspective, we have begun to
include mechanisms within the EDC to allow participants to inject
content into the simulations and adapt the environment to new
scenarios. The next steps include creating ways to link to existing
data and tools so that participants can draw on information from
their own areas of expertise to contribute to the emerging, shared
model. These developments will allow us to explore further
dimensions of a distributed intelligence framework within the
EDC.
4. ASSESSMENT AND IMPLICATIONS
Distributed intelligence is a fundamental concept to rethink the
relationship between humans and artifacts and to reinvent
thinking, working, learning, and collaborating in an informationrich world equipped with computational tools and linked together
with communication technologies. Numerous interesting
assessment challenges and implications exist — a few of them are
briefly discussed in the following paragraphs.
From Reflective Practitioners to Reflective Communities. In
today’s society “the goal of creating current-day Leonardos who
are competent in all of science” [Campbell, 1969; Shneiderman,
2002] has to fail because (as argued in this paper) the individual,
unaided human mind is limited. Knowledge is shifting from
individual minds to a collective social product only imperfectly
represented in any one mind: “Even within disciplines,
disciplinary competence is not achieved in individual minds, but
as a collective achievement made possible by the overlap of
narrow specialties” [Campbell, 1969]. We need to invent
alternative social organizations that will permit the flourishing of
narrow interdisciplinary specialties as well as new media to
support these reflective communities [National-Research-Council,
2003].
Quality of External Representations. In order to support
distributed intelligence effectively, we need criteria to assess the
quality, learnability, and effectiveness of external representation,
including the following attributes:

long-lasting (not ephemeral), allowing incremental
improvements over time;

produced, modified, reproduced, and evolved not only by
experts or technologists, but by the owners of the problem;

communicable over spatial, temporal, and conceptual
distances;

possessing computational capabilities (e.g., support for
multimodal representations, or context-awareness of users,
tasks, and situated environments); and

exploiting and complementing the strength of humans (e.g.,
sometimes visualizations make a big difference and
sometimes they do not).
Beyond Technology. How innovative are our ideas about the use
of new technologies supporting distributed intelligence?
Innovations should not be restricted to new technologies per se,
but they should support the co-evolution of social practices, new
media, and new learning organizations [Brown & Duguid, 2000].
To deeply understand the real impact of new technologies, we
need to shift the emphasis from a concern about who has access to
new technologies to who can use them in interesting ways for
personally meaningful tasks.
“Faustian Bargains”. All important technologies are “Faustian
bargains”: they give and take away and they produce winners and
losers. Socrates argued that reading and writing will weaken
human memory structures, and books will destroy thoughts. In our
work in the CLever project, we always have to be aware of
whether an over-reliance on tools for living will lead to learned
helplessness and deskilling, ruining humans’ native abilities by
making them dependent on tools.
The material culture around us often overwhelms people with its
richness. Based on the fundamental objective that the scarce
resource is not information, but human attention, the real design
challenges are (1) not to make the invisible visible, but to make
the important invisible visible, and (2) not to provide information
“anywhere at anytime to anyone,” but to say “the ‘right’
information at the ‘right’ time, in the ‘right’ place, in the ‘right’
way, to the ‘right’ person.” Wireless and mobile technologies and
pervasive computing environments allow us to be always
connected, support distributed intelligence by making tools for
living available at all times, and enable learning and using on
demand in situated environments. At the same time, however,
they can be intrusive and disruptive, leading to a state of
continuous partial attention and destroying the moments needed
for solitary reflection.
5. CONCLUSIONS
Distributed intelligence is anchored in the basic assumption that
the human mind cannot reach its potential unless it is immersed
and embedded in a distributed communication network (with
other humans and with artifacts) supported by socio-technical
environments. Visualizations have been one of the most important
contributions to external representations, allowing people to
understand and reason about complex activities. The last two
decades have seen far-reaching changes in living, learning,
working, and collaboration, fundamentally influenced by
information and communication technologies that make new
levels of distributed intelligence feasible and (potentially)
desirable. In order to make further progress, the communities
focused on advanced visual interfaces, human-computer
interaction, collaborative systems, and pervasive computing
should take up the challenge that the future is not “out there” to be
discovered (like Columbus discovered America), but has to be
invented and designed. Engaging in this challenge, we should not
only create and promote new technologies, but make major
contributions to fundamentally rethinking, reinventing, and
redesigning the role of humans in a technology-rich world, and
using our design capabilities and possibilities to improve the
human condition.
6. ACKNOWLEDGMENTS
The author thanks the members of the Center for LifeLong
Learning & Design (L3D) who have made substantial
contributions to the conceptual framework and systems described
in this paper. Special thanks go to: Stefan Carmien, Andrew
Gorman, and Jim Sullivan for the development of the CLever
projects; Ernesto Arias and Hal Eden for the development of the
EDC, and Elisa Giaccardi for the collaborative work on metadesign.
The research was sponsored by: (1) a grant from the Coleman
Institute, University of Colorado; (2) the National Science
Foundation, Grants (a) REC-0106976 “Social Creativity and
Meta-Design in Lifelong Learning Communities”; (b) CCR0204277 “A Social-Technical Approach to the Evolutionary
Construction of Reusable Software Component Repositories”; and
(c) IIS 0456053 “SGER: Designing and developing mobile
computing infrastructures and architectures to support people with
cognitive disabilities and caregivers in authentic everyday tasks”;
and (3) SRA Key Technology Laboratory, Inc., Tokyo, Japan.
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